Achievable Information Rates for Nonlinear Fiber Communication via End-to-end Autoencoder Learning

Abstract

Machine learning is used to compute achievable information rates (AIRs) for a simplified fiber channel. The approach jointly optimizes the input distribution (constellation shaping) and the auxiliary channel distribution to compute AIRs without explicit channel knowledge in an end-to-end fashion.

arXiv comment3 pages, 4 figures, fixed typos, revised layout

References in corpus (5)

• Deep Learning-Based Communication Over the Air
• End-to-end Deep Learning of Optical Fiber Communications
• Information capacity of optical fiber channels with zero average dispersion
• On Achievable Rates for Long-Haul Fiber-Optic Communications
• Design of APSK Constellations for Coherent Optical Channels with Nonlinear Phase Noise

Cited by in corpus (2)

• Cascade-Net: a New Deep Learning Architecture for OFDM Detection
• Blind interactive learning of modulation schemes: Multi-agent cooperation without co-design